Name
Abstract | ||
|---|---|---|
In this paper, we apply a combination of keyword-based information retrieval with a latent semantic-based model in Arabic-English Cross-Language Information Retrieval (CLIR). We aim at enabling Arabic-speaking users to access English content using their native language. Due to the complex morphology of Arabic, we use deep learning for both Arabic and English text to extract the deep semantics in the given languages and then use canonical correlation analysis (CCA) to project the semantic representations into a shared space in which retrieval can be done easily. We evaluate our system on selected articles from Wikipedia and show that the proposed combination can improve the state-of-the-art keyword-based CLIR performance. |
Year | Venue | Keywords |
|---|---|---|
2016 | KDIR: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 1 | Cross-language Information Retrieval,Deep Learning,Deep Belief Network,Canonical Correlation Analysis,Wikipedia,Arabic |
Field | DocType | Citations |
Human–computer information retrieval,Arabic,Information retrieval,Computer science,Natural language processing,Universal Networking Language,Artificial intelligence,Deep learning,Cross-language information retrieval | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar Attia | 1 | 0 | 0.34 |
| Michael Azmy | 2 | 0 | 0.34 |
| Ahmed Abu Emeira | 3 | 0 | 0.34 |
| Karim El Azzouni | 4 | 0 | 0.34 |
| Omar Hussein | 5 | 0 | 0.34 |
| Nagwa M. El-Makky | 6 | 63 | 11.48 |
| Khaled Nagi | 7 | 0 | 0.68 |